Covid Report for Family

Background

Notes and format last updated Apr 16, 2020

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines using rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines using rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	15,407
2	New Jersey	13,089
3	Massachusetts	8,743
4	Rhode Island	7,784
5	Connecticut	7,507
6	Louisiana	5,949
7	District of Columbia	5,817
8	Delaware	4,780
9	Michigan	4,041
10	Illinois	3,973
11	Pennsylvania	3,608
12	Maryland	3,448
13	South Dakota	2,684
14	Indiana	2,552
15	Colorado	2,549
16	Georgia	2,288
17	Mississippi	2,207
18	Iowa	2,168
19	Nebraska	2,003
20	Washington	1,888
21	Virginia	1,752
22	Nevada	1,601
23	Florida	1,545
24	New Mexico	1,532
25	New Hampshire	1,510
26	Tennessee	1,509
27	Ohio	1,480
28	Alabama	1,412
29	Utah	1,403
30	Vermont	1,381
31	North Dakota	1,355
32	Kansas	1,304
33	California	1,237
34	Missouri	1,232
35	South Carolina	1,142
36	Wisconsin	1,119
37	Arkansas	1,062
38	Idaho	1,024
39	Kentucky	1,015
40	Arizona	989
41	Texas	959
42	North Carolina	948
43	Oklahoma	877
44	Minnesota	823
45	Maine	785
46	Wyoming	698
47	West Virginia	618
48	Oregon	579
49	Alaska	482
50	Puerto Rico	448
51	Hawaii	427
52	Montana	421

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	New Jersey	271
2	Massachusetts	257
3	Rhode Island	254
4	Delaware	212
5	New York	199
6	Illinois	169
7	Iowa	142
8	Connecticut	140
9	Nebraska	129
10	District of Columbia	125
11	Maryland	125
12	Indiana	107
13	Pennsylvania	90
14	Michigan	87
15	New Mexico	77
16	Virginia	77
17	Mississippi	73
18	North Dakota	72
19	Colorado	71
20	Kansas	69
21	Louisiana	63
22	Minnesota	61
23	South Dakota	61
24	Georgia	57
25	New Hampshire	46
26	California	43
27	Tennessee	39
28	Utah	39
29	Ohio	38
30	North Carolina	35
31	Alabama	34
32	Kentucky	34
33	Wisconsin	34
34	Nevada	32
35	Washington	31
36	Arizona	30
37	Missouri	30
38	Texas	30
39	Florida	25
40	South Carolina	25
41	Arkansas	22
42	Wyoming	19
43	Oklahoma	18
44	Idaho	11
45	Maine	10
46	Oregon	10
47	West Virginia	10
48	Alaska	6
49	Puerto Rico	6
50	Vermont	5
51	Hawaii	1
52	Montana	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	926
2	New Jersey	762
3	Connecticut	608
4	Massachusetts	494
5	Louisiana	387
6	Michigan	367
7	District of Columbia	290
8	Rhode Island	236
9	Pennsylvania	186
10	Illinois	175
11	Maryland	162
12	Delaware	147
13	Indiana	143
14	Colorado	132
15	Washington	105
16	Georgia	102
17	Mississippi	84
18	Ohio	80
19	Nevada	76
20	Vermont	75
21	Virginia	61
22	Florida	56
23	Kentucky	56
24	Minnesota	56
25	Oklahoma	54
26	Alabama	53
27	Missouri	53
28	New Mexico	53
29	Wisconsin	52
30	California	49
31	New Hampshire	48
32	Iowa	46
33	Kansas	45
34	South Carolina	45
35	Arizona	42
36	Maine	38
37	Nebraska	35
38	North Carolina	34
39	Idaho	33
40	Tennessee	29
41	Texas	26
42	North Dakota	24
43	Oregon	23
44	West Virginia	22
45	Arkansas	19
46	Puerto Rico	16
47	Montana	14
48	South Dakota	14
49	Utah	14
50	Wyoming	12
51	Hawaii	11
52	Alaska	9

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	New Jersey	31
2	Massachusetts	24
3	Connecticut	22
4	New York	17
5	Pennsylvania	13
6	District of Columbia	12
7	Michigan	11
8	Louisiana	9
9	Delaware	8
10	Maryland	8
11	Illinois	7
12	Indiana	7
13	Rhode Island	7
14	Georgia	5
15	Ohio	5
16	Colorado	4
17	Iowa	3
18	Kentucky	3
19	Nevada	3
20	South Carolina	3
21	Alabama	2
22	California	2
23	Florida	2
24	Minnesota	2
25	Mississippi	2
26	Missouri	2
27	Nebraska	2
28	New Mexico	2
29	Virginia	2
30	Washington	2
31	Arizona	1
32	Kansas	1
33	New Hampshire	1
34	North Carolina	1
35	Oklahoma	1
36	Texas	1
37	West Virginia	1
38	Wisconsin	1
39	Alaska	0
40	Arkansas	0
41	Hawaii	0
42	Idaho	0
43	Maine	0
44	Montana	0
45	North Dakota	0
46	Oregon	0
47	Puerto Rico	0
48	South Dakota	0
49	Tennessee	0
50	Utah	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
New York	New York	101,217	1	99
Lincoln	Arkansas	62,500	2	99
Bledsoe	Tennessee	39,299	3	99
Rockland	New York	35,563	4	99
Marion	Ohio	33,736	5	99
Davidson	Tennessee	3,772	210	93
Richland	South Carolina	2,126	444	85
Pierce	Washington	1,555	649	79
Orange	California	732	1254	60
York	South Carolina	701	1295	58

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
New York	New York	7,567	1	99
Randolph	Georgia	2,803	2	99
Terrell	Georgia	2,110	3	99
Early	Georgia	2,061	4	99
Nassau	New York	1,531	5	99
Richland	South Carolina	72	575	81
Pierce	Washington	56	701	77
Davidson	Tennessee	36	915	70
Orange	California	14	1263	59
York	South Carolina	11	1316	58

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.